This invention relates to signal discrimination devices for discriminating input signals, such as radar reception signals, by means of neural networks.
It is an important technical problem to discriminate the species of an object from the radar reception signals. The values which are representative of certain features of the observed object are calculated, and, on the basis of these values, the observed object is discriminated into classes or categories, such as the categories of the surface of the earth, the surface of the sea, and a form of an airplane.
In the case of the conventional signal discrimination devices using neural networks, the codes for representing the discrimination categories are usually determined without any reference to the values of the parameters representing the categories.
For example, G. Vrckovnik, C. R. Carter, S. Haykin: "Radial Basis Function Classification of Impulse Radar Waveforms", Proceedings of International Joint Conference on Neural Networks, Vol. 1, pp. 45 through 50, San Diego, 1990 (referred to hereinafter as article (1)) shows a discrimination method using a neural network by which radar waveforms from asphalt covered bridge decks are classified into three categories: two-lift asphalt over concrete over rebars (2ACR); two-lift asphalt over a waterproofing membrane over concrete over rebars (2AMCR); and three-lift asphalt over concrete over rebars (3ACR).
FIG. 5 is a block diagram showing the structure of a conventional signal discrimination device using a neural network. FIG. 6 is a diagram showing the structure of the neural network. FIG. 7 is a diagram showing the relationship of the inputs and an output component of the neural network. FIG. 8 is a flowchart showing the procedure by which the codes for respective categories are generated in the code generator means of FIG. 5. FIG. 9 is a flowchart showing the procedure by which the discrimination result judgment means of FIG. 5 discriminates the categories in response to the outputs of the neural network. The structures and the procedures shown in these figures are those inferred from the disclosure of the above article (1).
As shown in FIG. 5, the input data for discrimination 2 and the input data for learning 3 are coupled selectively to the neural network 8 via a learning/discrimination change-over switch 7. The output of a code generator means 11 is selectively coupled to the neural network 8 and the discrimination result judgment means 9 via a switch interlocked with the learning/discrimination change-over switch 7. The output of the discrimination result judgment means 9 are stored in a data file for discrimination result judgement 4.
As shown in FIG. 6, the neural network 8 includes: an input layer 22 consisting of a plurality of input nodes or input elements; an intermediate layer 23 consisting of intermediate nodes 21 provided with respective weights; and an output layer 24 consisting of output elements 20.
The output (0) 31 of each output node 28 of the neural network 8 is generated as shown in FIG. 7. Namely, a plurality of input data, i.sub.1, i.sub.2, i.sub.3, - - - , i.sub.M applied to the input nodes 27 are weighted by the respective weights 29, w.sub.1, w.sub.2, w.sub.3, - - - , w.sub.M. The weighted sum of inputs: ##EQU1## is calculated by the summation means 25.
Then, the output data 31 is calculated by means of the conversion function 26: and is output from each output node 28.
Next, the operation of the signal discrimination device of FIGS. 5 through 7 is described. It is assumed that the inputs are classified into three categories: (1) two-lift asphalt over a waterproofing membrane over concrete over rebars (2AMCR); (2) two-lift asphalt over concrete over rebars (2ACR); and (3) three-lift asphalt over concrete over rebars (3ACR).
First, the code generator means 11 determines the codes of the respective categories as shown in TABLE 1 in accordance with the procedure shown in FIG. 8.
TABLE 1 ______________________________________ category code ______________________________________ 2AMCR 100 3ACR 010 2ACR 001 ______________________________________
At step S60 in FIG. 8, the code length L of the output of the neural network 8 is set equal to the number N of classification categories into which the inputs are to be classified via the neural network 8. Then, at step S61, the components C.sub.ij (where j ranges from 1 to the code length L) of the code C.sub.i for the ith category are set by: ##EQU2##
Thus, the codes for the respective categories are determined as shown in TABLE 1. Each code is determined under the assumption that only one of its components takes the value 1.
Next, the learning/discrimination change-over switch 7 is switched to the learning side to train the neural network 8. By means of this learning or training procedure, the neural network 8 learns to output the codes as shown in TABLE 1 for respective categories in response to the input data for learning. The neural network 8 receives sets of input data for learning 3 and the codes corresponding thereto as shown in TABLE 1 generated by the code generator means 11. (Each set of the input data for learning 3 consists of a number of sample values composing a radar reception signal waveform.) In response to the sets of the input data for learning 3 and the codes corresponding thereto, the neural network 8 modifies the weights of the intermediate nodes 21 in accordance with the well-known error back propagation algorithm or the radial basis function algorithm described in the above article (1). After the modification of the weights of the intermediate nodes 21 is completed, the neural network 8 is trained to output the codes C.sub.i as shown in TABLE 1 for the respective categories in response to the input data for learning 3.
When the learning is completed, the learning/discrimination change-over switch 7 is switched to the discrimination side to discriminate or classify each set of input data for discrimination 2 into the categories (1) through (3). (Each set of the input data for discrimination 2 consists of a number of samples of a radar reception signal waveform) thus, in response to the input data for discrimination 2, the neural network 8 outputs signals each consisting of three values approximately equal to the codes C.sub.i for the respective categories. These outputs of the neural network 8 are generated via the data conversion shown in FIG. 7. The discrimination result judgment means 9 receives the output codes of the neural network 8 and the codes generated by the code generator means 11, and determines the category or classification of each set of the input data for discrimination 2.
FIG. 9 is a flowchart showing the procedure by which the discrimination result judgment means of FIG. 5 discriminates the categories in response to the outputs of the neural network. At step S50, a neural network output O (consisting of three values) and the codes C.sub.i (i=1 through N) for respective categories as shown in TABLE 1 are input to the discrimination result judgment means 9. Next, at step S51, the discrimination result judgment means 9 determines the distance D.sub.i (i=1 through N): ##EQU3## where O.sub.j is the output value of the jth element of the output elements 20 of the neural network 8, C.sub.ij is the jth component of the C.sub.i.
Then, at step S52, the discrimination result judgment means 9 determines the minimum D.sub.I among the D.sub.i (i=1 through N). Further, at step S53, the ith category is identified as the category to which the input data for discrimination 2 is classified.
The above conventional signal discrimination device has the following disadvantage. Namely, the distances between the codes for respective categories are determined without any relation to the degrees of affinity between the categories.
FIG. 10 is a diagram showing the distances between the codes for respective categories for the signal discrimination device of FIG. 5. The codes of length L can be represented as points in L dimensional Euclidean space. Thus, in FIG. 10, the codes C.sub.i (i=1 through 3) for three categories are represented as points 35, 36, and 37, respectively, in the 3-dimensional Euclidean space. The distances AB, AC, and BC between the categories A and B, between the categories A and C, and between the categories B and C, respectively, are equal to each other, although, for example, the relation between the categories B and C is less close than the relations between the categories A and B or between the categories A and C, and hence the discrimination or classification error is more grave between the categories B and C than between the categories A and B or the categories A and C. Thus, the probability of an occurrence of a grave classification error (between the categories B and C) is as high as the probability of an occurrence of less grave classification errors (between the categories A and B or between the categories A and C). Classification errors generally occur at random (with equal probability between the categories B and C and between the categories A and B or between the categories A and C), due to the noises in the input data for discrimination 2 or failures of elements within the neural network 8.